Assessment of

Trabecular Bone Microstructure

using Dental Cone Beam CT

Norliza Ibrahim

2

The studies in this thesis were conducted at the section of Oral Radiology of the Academic

Centre for Dentistry Amsterdam (ACTA), Vrije University Amsterdam, The Netherlands.

The author was financially supported with a scholarship by the University of Malaya, Kuala

Lumpur, Malaysia.

Financial support for printing:

- ACTA Graduate School of Dentistry

- Oral Radiology Foundation Amsterdam (ORFA)

© Norliza Ibrahim, Amsterdam, 2014. All rights reserved.

Cover design: Norliza Ibrahim

Printed by Gildeprint Drukkerijen, Enschede.

ISBN: 9789461086082

3

VRIJE UNIVERSITEIT

Assessment of

Trabecular Bone Microstructure

using Dental Cone Beam CT

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad Doctor aan

de Vrije Universiteit Amsterdam,

op gezag van de rector magnificus

prof.dr. F.A. van der Duyn Schouten,

in het openbaar te verdedigen

ten overstaan van de promotiecommissie

van de Faculteit der Tandheelkunde

op vrijdag 21 maart 2014 om 9.45 uur

in de aula van de universiteit,

De Boelelaan 1105

door

Norliza Binti Ibrahim

geboren te Selangor, Maleisië

4

promotor: prof.dr. P.F. van der Stelt

co promotor: dr. B.A. Hassan

5

To my parents,

Haji Ibrahim Sharif and

Hajjah Ramlah Arifin.

6

7

Table of Contents

CHAPTER 1 INTRODUCTION 9

CHAPTER 2 ACCURACY OF TRABECULAR BONE

MICROSTRUCTURAL MEASUREMENT USING CONE

BEAM CT DATASETS

25

CHAPTER 3 BONE QUALITY EVALUATION AT DENTAL IMPLANT

SITE USING MULTI-SLICE CT, MICRO-CT AND CBCT

41

CHAPTER 4 INFLUENCE OF SCAN PARAMETERS ON CBCT

TRABECULAR BONE MICROSTRUCTURAL

MEASUREMENTS

63

CHAPTER 5 INFLUENCE OF OBJECT LOCATION ON CBCT

MICROSTRUCTURAL ASSESSMENTS

81

CHAPTER 6 CBCT AND MICRO CT ASSESSMENTS OF TRABECULAR

BONE MICROSTRUCTURE AT DIFFERENT

MANDIBULAR REGIONS

97

CHAPTER 7 DISCUSSION 111

CHAPTER 8 SUMMARY AND CONCLUSIONS 125

CHAPTER 9 SAMENVATTING EN CONCLUSIES 129

PUBLICATIONS 133

CHAPTER 10

ACKNOWLEDGMENTS

135

8

9

Chapter 1

Introduction

Part of this chapter has been published as:

Ibrahim N, Parsa A, Hassan B, van der Stelt P, Wismeijer D. Diagnostic imaging of

trabecular bone microstructure for oral implants: a literature review.

Dentomaxillofacial Radiology 2013; 42: 20120075.

10

11

Current and future trends of CBCT in implant dentistry

Presurgical radiographic assessment is essential in planning dental implant treatment1

and dental implant thread design.2 Currently, the role of cone beam CT (CBCT) for

dental implant treatment is gradually increasing due to the wide accessibility and the

advantages obtained from these systems.3,4

CBCT assessments, however, are

generally focused on bone density5,6

and linear bone measurement.7 Structural

properties of bone are much less involved in CBCT evaluations.

The structural properties of trabecular bone are amongst the significant determinants

for bone strength. The assessment of trabecular bone structures is recommended

when predicting an implant success8,9

owing to its significant role in the healing and

osseointegration process at the implant-bone surface.10

However, studies on trabecular

microstructure assessment using CBCT are scarce because of the low scanning

resolution11

of the earlier generation of CBCT devices.

The latest CBCT equipment with a resolution of 80µm may probably serve as a 3D

imaging modality for the clinical assessment of the anisotropic trabecular

microstructures.12

Subsequently, this chapter reviews the imaging modalities for

trabecular bone microstructure in oral implants and the potential of CBCT for

microstructural assessments.

Diagnostic imaging of trabecular bone microstructure for oral implants

The term ‘bone quality’ has been extensively used in the literature to describe

different aspects of bone characteristics with variable definitions depending on the

utilized context. Among inseparable factors that influence bone quality is the

trabecular bone.13-16

The trabeculae or ‘trabecular’ is the primary anatomical and

functional unit of cancellous bone. Cortical bone helps to attain primary implant

stability, but the role of cancellous bone is also remarkable. This is because

cancellous bone has a higher bone turnover rate than cortical bone17

and has a direct

contact with majority of the implant surface.8 Accordingly, it influences the healing

and osseointegration process at the implant-bone surface.10

12

Bone strength has a significant role in determining implant success. To improve

prediction of bone strength, the measurements of trabecular density and trabecular

microstructure should be combined.18

This is because those measurements do not

always denote each other. For instance, a high bone density does not always

correspond to high trabecular parameters such as trabecular number (Tb.N) and

trabecular thickness (Tb.Th).19

Therefore, estimating implant success by assessing

the trabecular density alone is no longer suggested.9

Precise clinical assessment of bone structural and mechanical properties is essential in

planning dental implant treatment and implant thread design.2 The task can be

performed on two-dimensional (2D) plain radiographs (e.g. intra oral radiograph) by

calculating fractal dimensions of the trabecular bone.20

In three-dimensional (3D)

imaging modalities (e.g. HR-pQCT) the high resolution images are analyzed using

dedicated imaging software (e.g. CT Analyser [CTAn]; Skyscan®, Kontich,

Belgium). Computational techniques such as finite element methods (FEM)21,22

are

also utilized in analyzing 3D images to simulate the status of implant surface and the

bone adjacent to the implant.2

To date, bone quality assessments in oral implant studies have largely focused on

trabecular bone density.5,23-25

What follows is a review of the imaging techniques used

in oral implant studies for assessing the trabecular microstructures as evidenced in the

literature. Articles reported on trabecular microstructural imaging methods were

searched in PubMed electronic database. Titles and abstracts of the related articles

were reviewed base on keywords that had initially been set as inclusion criteria: bone

quality, imaging, trabecular microstructure, cone beam CT and dental or oral implant.

1. Dental Radiographs

Periapical (PA) and panoramic (OPG) radiographs are the first-choice diagnostic

clinical instruments in dentistry. PA radiographs with superior resolution and

sharpness provide valuable information for evaluating the amount and pattern of

trabecular bone structure.26,27

Trabecular visibility was reported to be high on PA

radiographs, 28

thus enhancing its potential in trabecular imaging studies.29-34

13

Bone classification systems are used to study bone quality on PA images. Of the

Lekholm & Zarb, Trisi & Rao and Misch systems, the first is largely adopted in oral

implant studies on trabecular bone assessment.29-32

A visual index was proposed in

1996 to simplify trabecular classification on PA radiographs.30

This index categorizes

trabecular pattern according to the intertrabecular spaces (small or large) and degree

of trabeculation (sparse or dense).32-34

However, these subjective techniques remain

partially validated.29

On the other hand, panoramic radiographs have also been used to

assess trabecular structure.35-36

However, the technique applies the rotational

principles that structures not centered in the focal trough are not sharply imaged. The

formation of geometrical distortion, magnification and loss of information are thus

commonly observed artifacts on panoramic radiographs. Moreover, the reduced

resolution of panoramic images degrades its ability in identifying fine trabeculae.37

Therefore, its applications in trabecular assessments are less favorable than PA

radiographs.34

Undeniably, utilizing dental radiographs for assessing trabecular microstructure is a

rapid, relatively safe and convenient method to apply in the jaws. Although the nature

of the 2D image could never provide information in the bucco-lingual direction,38

dental radiographs are still largely employed in many countries for pre implant

assessment due to availability and cost. 39

The complex shapes and structure of trabecular bone can be calculated by performing

fractal dimension (FD) analysis on 2D images such as periapical and panoramic

radiographs.37

Current studies on 2D FD analysis of trabecular microarchitecture

parameters (porosity, connectivity and anisotropy) are reported to be adequately

comparable to that of 3D FD method.40

FD analyses and calculations of trabecular

structures require several complex steps.32

Nowadays, the FD applications are

simplified by using personal computers and a simple JAVA software (Oracle®, Los

Angeles, CA). However, the overall reproducibility of the projection techniques

remains as contentious issue that requires further investigations.41

14

2. Magnetic Resonance Imaging (MRI)

MRI is a non-invasive, non-ionizing system which applies high magnetic fields,

transmission of radiofrequency waves and detection of radiofrequency signals from

excited hydrogen protons. Trabecular bone is filled with bone marrow that contains

free protons and generates a strong MR signal.42,43

Fat and water protons in the

marrow tissue are depicted as negative image. Because trabecular structure can not

directly be visualized, this technique employs image processing to invert the negative

image.44,45

Using this technique, values for implant loading and bone healing time at

trabecular alveolar bone were proposed to improve implant success.46

Despite

improving the trabecular structure assessment, the quality of the acquired MR images

is largely influenced by the field strength, pulse sequence, echo time, and signal to

noise ratio (SNR). Additionally, the measurements are affected by the selected

threshold values, image processing algorithms, complex analysis and interpretation of

the images.46-48

Moreover, the availability and accessibility of MRI machines for the

dental practitioners remains limited.

3. Computed Tomography (CT)

Computed tomography techniques are being progressively developed to meet the

clinical needs in assessing bone microstructure. Structural analysis of trabecular bone

requires scanners with contiguous isotropic pixel resolution of less than 300μm.49

High resolution CT systems that are commonly employed for trabecular

microstructural assessment in oral implant studies are discussed below.

3.1 Multi Detector Computed Tomography (MDCT)

The latest generation of MDCT system has improved its resolution to 150-300μm in

plane and 300-500μm in slice thickness.50

Trabecular microstructure parameters such

as trabecular number (Tb.N), trabecular thickness (Tb.Th) and trabecular separation

(Tb.Sp) were measured using MDCT and compared to high-resolution peripheral

quantitative CT (HR-pQCT).49

Although the resolution is still beyond trabecular

dimensions (50-200µm), the measurements from both techniques were highly

correlated. In a human cadaver study, trabecular microstructure parameters were

15

compared among MDCT and micro-CT and micro-CT FE modelling.51

The study

concluded that trabecular bone structure assessment using MDCT is overall feasible,

although still limited by its spatial resolution. These studies were conducted using a

high-resolution mode, which is not routinely used in clinical settings protocols.49-50

Consequently, although MDCT is largely employed in oral implant studies, its

applicability remains mostly confined to bone density measurements. 52-54

3.2 High-resolution peripheral Quantitative Computed Tomography (HR-

pQCT)

With a spatial resolution of 82μm, this device is used for trabecular microstructural

imaging. The measurements of microstructural parameters are reported to be

comparable with that of micro-CT (voxel size of 25μm).55

The technology has a

higher spatial resolution than MDCT; however, scanning sites are limited to

peripheral skeletal region (e.g. wrist and tibia) and accessibility is currently limited.50

Unlike MRI technique, microstructural assessment using high-resolution CT permits

direct visualization of the trabecular bone. However, the later technique involves a

relatively high radiation dose which is beyond the recommended clinical setting.45

Moreover, the results are also affected by the selected threshold, image analysis and

processing techniques.56

Thus, its application in oral implant imaging studies remains

restricted.

3.3 Micro Computed Tomography (Micro-CT)

Two-dimensional histomorphometric analysis was previously considered as the gold

standard for assessing trabecular size, shape, connectivity and orientation. As it is

time consuming and costly, micro-CT is now routinely employed for structural 2D or

3D evaluations of trabecular microstructure.50,57

This non-destructive high resolution

(approaching 10µm) method depicts trabecular network in different gray levels

according to its mineral content. It has been reported that the trabecular parameters

quantified by micro-CT are comparable to the traditional 2D histomorphometric

values.42,43

As it permits high resolution scans, in 2004 micro-CT has been

recommended as a gold standard imaging for ex-vivo bone studies at implant sites.57

16

However, only studies with small jaw specimens were conducted to observe

trabecular microstructure in oral implant research.5,8,19,58

3.4 Cone beam computed tomography (CBCT)

Cone beam computed tomography systems were developed in the1990s. In 2001,

CBCT was introduced as a 3D imaging modality. Since then it has largely replaced

both single- and multi- slice CT for diagnostic imaging in oral implants. 59

Owing to

the wide availability of the machines, rapid scan and processing time, high resolution

images and relatively reduced scan radiation dose and costs, the demand for CBCT

images preceding implant placement has increased exponentially.3,60-63

Although

many studies have been conducted on CBCT, the literature on its suitability in

measuring trabecular bone microstructural parameters at oral implant site remains

scarce. This may be due to the insufficient resolution of the past generations of CBCT

systems to depict bone microstructure. The applications of CBCT in evaluating bone

quality are still restricted on bone density assessment.52-54

Recently, however, a study

on assessing bone microstructure described CBCT as a promising modality for

analyzing trabecular bone.63

Bone parameters (Tb.Th, Tb.N and Tb.Sp) at mandibular

condyle were also successfully evaluated by CBCT at a resolution of 125µm coupled

with image processing.64

The visibility of small anatomical structures with CBCT is largely influenced by the

field of view (FOV) and scan setting selection.65

Visibility of trabecular

microstructure is mainly determined by the chosen voxel size and SNR plus image

artifacts.66

In CBCT, voxel size and slice thickness, spatial and contrast resolutions

vary with respect to machine type, FOV and scan settings. 65,66

Additionally, several

image artifacts specific to CBCT technology could influence the effective system

resolution, which could be lesser than the nominal system resolution expressed in

voxel size alone. It has been previously stated that the accuracy of 3D measurement of

anisotropic trabecular structure can be improved by performing in-vivo rather than in-

vitro investigation.16,18

In this respect, the use of CBCT could prove appealing. As the

need to evaluate the implant insertion sites prior to surgical placement has

dramatically increased, CBCT should be validated as a non-invasive procedure for

assessing bone microstructure.

17

Figure 1 Sagittal images of trabecular structure at the lingual foramen region derived

from A: MDCT (650μm), B: CBCT (80μm) and C: Micro CT (35μm).

Müller et al. has described that a CT scanner with a resolution up to 60μm can present

morphometric information comparable to that of 10 μm.67

Using the latest CBCT

system, the appearance of trabecular structure was observed using a 4x4cm FOV at a

nominal resolution of 80µm. The resultant image was compared with images derived

from MDCT and micro-CT (Figure 1). It is expected that this system could be useful

in measuring trabecular microstructures. However, thorough investigation and

validation are required prior to applying this technique in clinical practice.

Although there is a rapid progress in advanced bone imaging modalities, their routine

clinical employment remains limited due to the technical features, cost and complex

procedures. The current review recommends studies to validate CBCT as a clinical

imaging modality to evaluate trabecular microstructure at oral implant sites.

Aims of the thesis:

The primary aim of this thesis is to validate the applicability and accuracy of CBCT

for trabecular bone microstructural assessments. Additionally, the consistency of

structural analysis is assessed using various scanning protocols, different sites of the

mandible and different locations of the object in the CBCT field FOV. Therefore, the

current studies are conducted according to the following research questions:

18

1. What is the accuracy of CBCT trabecular bone microstructure measurements in

comparison with µCT?

2. What is the accuracy of CBCT trabecular bone density and bone volume fraction

in comparison with MSCT and µCT?

3. What is the effect of scan parameters (FOV, rotation steps and resolution) on

CBCT trabecular bone microstructural measurements?

4. What is the influence of the object location on CBCT trabecular bone

microstructure measurements?

5. What is the difference between CBCT and µCT in measuring trabecular bone

microstructure at different sites of edentulous mandibles?

19

References:

1. Jacobs R, Van Steenberghe D. Radiographic planning and assessment of endosseous

oral implants. Berlin: Springer, 1997.

2. DeTolla DH, Andreana S, Patra A, Buhite R, Comella B. Role of the finite element

model in dental implants. J Oral Implantol 2000; 26: 77-81.

3. De Vos W, Casselman J, Swennen GR. Cone-beam computerized tomography

(CBCT) imaging of the oral and maxillofacial region: a systematic review of the

literature. Int J Oral and Maxillofac Surg 2009; 38: 609-625.

4. Guerrero ME, Jacobs R, Loubele M, Schutyser F, Suetens P, van Steenberghe D.

State-of-the-art on cone beam CT imaging for preoperative planning of implant

placement. Clin Oral Investig 2006; 10: 1-7.

5. González-García R, Monje F. The reliability of cone-beam computed tomography to

assess bone density at dental implant recipient sites: a histomorphometric analysis by

micro-CT. Clin Oral Implants Res 2012 doi: 10.1111/j.1600-0501.2011.02390.x.

6. Monje A, Monje F, González-García R, Galindo-Moreno P, Rodriguez-Salvanes F,

Wang HL. Comparison between microcomputed tomography and cone-beam

computed tomography radiologic bone to assess atrophic posterior maxilla density

and microarchitecture. Clin Oral Implants Res. 2013 Feb 26. doi: 10.1111/clr.12133.

7. Quereshy FA, Savell TA, Palomo JM. Applications of cone beam computed

tomography in the practice of oral and maxillofacial surgery. Journal of Oral

Maxillofacial Surgery 2008; 66: 791-796.

8. Fanuscu MI, Chang TL. Three-dimensional morphometric analysis of human cadaver

bone: microstructural data from maxilla and mandible. Clin Oral Impl Res 2004; 15:

213-218.

9. Wirth AJ, Goldhahn J, Flaig C, Arbenz P, Müller R, van Lenthe GH. Implant stability

is affected by local bone microstructural quality. Bone 2011; 49: 473-478.

10. Minkin C, Marinho VC. Role of the osteoclast at the bone-implant interface. Adv Dent

Res 1999; 13: 49-56.

11. Kothari M, Keaveny TM, Lin JC, Newitt DC, Genant HK, Majumdar S. Impact of

spatial resolution on the prediction of trabecular architecture parameters. Bone 1998;

22: 437-443.

12. Müller R, Van Campenhout H, Van Damme B, Van Der Perre G, Dequeker J,

Hildebrand T, et al. Morphometric analysis of human bone biopsies: a quantitative

20

structural comparison of histological sections and micro-computed tomography. Bone

1998; 23:59–66.

13. Sievänen H, Kannus P, Järvinen TLN. Bone quality: An empty term. PLoS Med 2007;

4: e27.

14. Compston J. Bone Quality: What is it and how is it measured? Arq Bras Endocrinol

Metab 2006; 50: 579-585.

15. Licata A. Bone density vs bone quality: What’s a clinician to do? Cleve Clin J Med

2009; 76: 331-336.

16. Fyhrie DP. Summary - Measuring ‘‘bone quality’’. J Musculoskelet Neuronal Interact

2005; 5: 318-320.

17. Sakka S, Coulthard P. Bone quality: a reality for the process of osseointegration.

Implant Dent 2009; 18: 480-485.

18. Müller R. Bone microarchitecture assessment: current and future trends. Osteoporos

Int 2003; 14 Suppl 5:S89-S95.

19. Gomes de Oliveira RC, Leles CR, Lindh C, Ribeiro-Rotta RF. Bone tissue

microarchitectural characteristics at dental implant sites. Part 1: Identification of

clinical-related parameters. Clin Oral Implants Res 2011; 000-000. doi:

10.1111/j.1600-0501.2011.02243.x.

20. Yi WJ, Heo MS, Lee SS, Choi SC, Huh KH. Comparison of trabecular bone

anisotropies based on fractal dimensions and mean intercept length determined by

principal axes of inertia. Med Biol Eng Comput 2007; 45: 357-364.

21. Krug R, Burghardt AJ, Majumdar S, Link TM. High-resolution imaging techniques

for the assessment of osteoporosis. Radiol Clin North Am 2010; 48: 601-621.

22. Müller R, van Lenthe GH. Trabecular bone failure at the microstructural level. Curr

Osteoporos Rep 2006; 4: 80-86.

23. Turkyilmaz I, McGlumphy EA. Influence of bone density on implant stability

parameters and implant success: a retrospective clinical study. BMC Oral Health

2008; 8: 32.

24. Turkyilmaz I, Sennerby L, McGlumphy EA, Tözüm TF. Biomechanical aspects of

primary implant stability: a human cadaver study. Clin Implant Dent Relat Res 2009;

11: 113-119.

25. Molly L. Bone density and primary stability in implant therapy. Clin Oral Implants

Res 2006; 17: 124-135.

21

26. White SC, Pharoah MJ. Oral radiology: principles and interpretation (5th edn). St.

Louis: Mosby, 2004.

27. Whaites E. Essentials of dental radiography and radiology (3rd edn). London:

Churchill Livingstone, 2007.

28. Couture RA, Whiting BR, Hildebolt CF, Dixon DA. Visibility of trabecular structures

in oral radiographs. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2003; 96:

764–771.

29. Ribeiro-Rotta RF, Lindh C, Pereira AC, Rohlin M. Ambiguity in bone tissue

characteristics as presented in studies on dental implant planning and placement: a

systematic review. Clin Oral Implants Res 2011; 22: 789-801.

30. Lindh C, Petersson A, Rohlin M. Assessment of the trabecular pattern before

endosseous implant treatment: diagnostic outcome of periapical radiography in the

mandible. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 1996; 82: 335–343.

31. Aalam AA, Nowzari H, Krivitsky A. Functional restoration of implants on the day of

surgical placement in the fully edentulous mandible: a case series. Clin Implant Dent

Relat Res 2005; 7: 10-16.

32. Jonasson G, Jonasson G, Jonasson L, Kiliaridis S. Skeletal bone mineral density in

relation to thickness, bone mass, and structure of the mandibular alveolar process in

dentate men and women. Eur J Oral Sci 2007; 115: 117-123.

33. Lindh C, Horner K, Jonasson G, Olsson P, Rohlin M, Jacobs R, et al. The use of

visual assessment of dental radiographs for identifying women at risk of having

osteoporosis: the OSTEODENT project. Oral Surg Oral Med Oral Pathol Oral

Radiol Endod 2008; 106: 285–293.

34. Pham D, Jonasson G, Kiliaridis S. Assessment of trabecular pattern on periapical and

panoramic radiographs: A pilot study. Acta Odontol Scand 2010; 68: 91-97.

35. Taguchi A, Suei Y, Ohtsuka M, Otani K, Tanimoto K, Ohtaki M. Usefulness of

panoramic radiography in the diagnosis of postmenopausal osteoporosis in women.

Width and morphology of inferior cortex of the mandible. Dentomaxillofac Radiol

1996; 25: 263-267.

36. Watanabe PCA, Oliveira TM, Monteiro SAC, Iyomasa MM, Regalo SCH, Siessere S.

Morphodigital study of the mandibular trabecular bone in panoramic radiographs. Int

J Morphol 2007; 25: 875-880.

37. Bollen AM, Taguchi A, Hujoel PP, Hollender LG. Fractal dimension on dental

radiographs. Dentomaxillofac Radiol 2001; 30: 270-275.

22

38. Lofthag-Hansen S, Gröndahl K, Ekestubbe A. Cone-beam CT for preoperative

implant planning in the posterior mandible: visibility of anatomic landmarks. Clin

Implant Dent Relat Res 2009; 11: 246-255.

39. Sakakura CE, Morais JA, Loffredo LC, Scaf G.A survey of radiographic prescription

in dental implant assessment. Dentomaxillofac Radiol 2003; 32: 397-400.

40. Pothuaud L, Benhamou CL, Porion P, Lespessailles E, Harba R, Levitz P. Fractal

dimension of trabecular bone projection texture is related to three-dimensional

microarchitecture. J Bone Miner Res 2000; 15: 691-699.

41. Sánchez I, Uzcátegui G. Fractals in dentistry. J Dent 2011; 39: 273-292.

42. Genant HK, Jiang Y. Advanced imaging assessment of bone quality. Ann N Y Acad

Sci 2006; 1068: 410-428.

43. Lespessailles E, Chappard C, Bonnet N, Benhamou CL. Imaging techniques for

evaluating bone microarchitecture. Joint Bone Spine 2006; 73: 254-261.

44. Jiang Y, Zhao J, White DL, Genant HK. Micro CT and Micro MR imaging of 3D

architecture of animal skeleton. J Musculoskelet Neuronal Interact 2000; 1: 45-51.

45. Ito M. Recent progression in assessment of bone microstructure. Nihon Rinsho 2011;

691233-691238.

46. Choël L, Last D, Duboeuf F, Seurin MJ, Lissac M, Briquet A, et al. Trabecular

alveolar bone microarchitecture in the human mandible using high resolution

magnetic resonance imaging. Dentomaxillofac Radiol 2004; 33: 77–182.

47. Sell CA, Masi JN, Burghardt A, Newitt D, Link TM, Majumdar S. Quantification of

trabecular bone structure using magnetic resonance imaging at 3 Tesla-calibration

studies using microcomputed tomography as a standard of reference. Calcif Tissue Int

2005; 76: 355-364.

48. Celenk P, Celenk C. Evaluation by quantitative magnetic resonance imaging of

trabecular bone quality in mandible and cervical vertebrae. Clin Oral Implants Res

2010; 21: 409-413.

49. Issever AS, Link TM, Kentenich M, Rogalla P, Burghardt AJ, Kazakia GJ, et al.

Assessment of trabecular bone structure using MDCT: comparison of 64- and 320-

slice CT using HR-pQCT as the reference standard. Eur Radiol 2010; 20: 458-468.

50. Burghardt AJ, Link TM, Majumdar S. High-resolution computed tomography for

clinical imaging of bone microarchitecture. Clin Orthop Relat Res 2011; 469: 2179-

2193.

23

51. Issever AS, Link TM, Kentenich M, Rogalla P, Schwieger K, Huber MB, et al.

Trabecular bone structure analysis in the osteoporotic spine using a clinical in vivo

setup for 64-slice MDCT imaging: comparison to micro CT imaging and micro FE

modelling. J Bone Miner Res 2009; 24:1628-1637.

52. Naitoh M, Aimiya H, Hirukawa A, Ariji E. Morphometric analysis of mandibular

trabecular bone using cone beam computed tomography: an in vitro study. Int J Oral

Maxillofac Implants 2010; 25: 1093-1098.

53. Naitoh M, Hirukawa A, Katsumata A, Ariji E. Prospective study to estimate

mandibular cancellous bone density using large-volume cone-beam computed

tomography. Clin Oral Implants Res 2010; 21: 1309-1313.

54. Araki K, Okano T. The effect of surrounding conditions on pixel value of cone beam

computed tomography. Clin Oral Impl Res 2011; 00:1-4.

55. Liu XS, Zhang XH, Sekhon KK, Adams MF, McMahon DJ, Bilezikian JP, et al.

High-Resolution Peripheral Quantitative Computed Tomography can assess

microstructural and mechanical properties of human distal tibial bone. J Bone Miner

Res 2010; 25: 746–756.

56. MacNeil JA, Boyd SL. Accuracy of high-resolution peripheral quantitative computed

tomography for measurement of bone quality. Med Eng Phys 2007; 29: 1096-1105.

57. Yip G, Schneider P, Roberts EW. Micro-Computed Tomography: High resolution

imaging of bone and implants in three dimensions. Semin Orthod 2004; 10: 174-187.

58. Swain MV, Xue J. State of the art of micro CT applications in dental research. Int J

Oral Sci 2009; 1: 177-188.

59. Hatcher DC. Operational principles for cone-beam computed tomography. J Am Dent

Assoc 2010; 141 Suppl 3:3S-6S.

60. Al-Rawi B, Hassan B, Vandenberge B, Jacobs R. Accuracy assessment of three-

dimensional surface reconstructions of teeth from Cone Beam Computed Tomography

scans. J Oral Rehabil 2010; 37: 352-358.

61. Kau CH, Bozic M, English J, Lee R, Bussa H, Ellis RK. Cone-beam computed

tomography of the maxillofacial region - an update. Int J Med Robot 2009; 5: 366-

380.

62. Liang X, Jacobs R, Hassan B, Li L, Pauwels R, Corpas L, et al. A comparative

evaluation of Cone Beam Computed Tomography (CBCT) and Multi-slice CT

(MSCT) Part I. on subjective image quality. Eur J Radiol 2010; 75: 265-269.

24

63. Corpas LS, Jacobs R, Quirynen M, Huang Y, Naert I, Duyck J. Peri-implant bone

tissue assessment by comparing the outcome of intra-oral radiograph and cone beam

computed tomography analyses to the histological standard. Clin Oral Impl Res 2011;

22: 492-499.

64. Liu SM, Zhang ZY, Li JP, Liu DG, Ma XC. A study of trabecular bone structure in

the mandibular condyle of healthy young people by cone beam computed

tomography. Zhonghua Kou Qiang Yi Xue Za Zhi 2007; 42: 357-360.

65. Loubele M, Jacobs R, Maes F, Denis K, White S, Coudyzer W, et al. Image quality vs

radiation dose of four cone beam computed tomography scanners. Dentomaxillofac

Radiol 2008; 37: 309-318.

66. Schulze R, Heil U, Gross D, Bruellmann DD, Dranischnikow E, Schwanecke U, et al.

Artefacts in CBCT: a review. Dentomaxillofac Radiol 2011; 40: 265-273.

67. Müller R, Koller B, Hildebrand T, Laib A, Gianolini S, Rüegsegger P. Resolution

dependency of microstructural properties of cancellous bone based on three-

dimensional μ-tomography. Technol Health Care 1996; 4: 113–119.

25

Chapter 2

Accuracy of trabecular bone

microstructural measurement using

cone-beam CT datasets

This chapter has been published as:

Ibrahim N, Parsa A, Hassan B, van der Stelt P, Aartman IHA,Wismeijer D. Accuracy of

trabecular bone microstructural measurement at planned dental implant sites using cone-beam

CT datasets. Clinical Oral Implants Research. doi: 10.1111/clr.12163.

26

Accuracy of trabecular bone microstructural

measurement at planned dental implant sites using

cone-beam CT datasets

Norliza Ibrahim, Azin Parsa, Bassam Hassan, Paul van der Stelt, Irene

HA Aartman, Daniel Wismeijer.

Clinical Oral Implants Research. doi: 10.1111/clr.12163

Summary

Objective: Cone-beam CT (CBCT) images are infrequently utilized for trabecular

bone microstructural measurement due to the system's limited resolution. The aim of

this study was to determine the accuracy of CBCT for measuring trabecular bone

microstructure in comparison with micro CT (μCT).

Materials and methods: Twenty-four human mandibular cadavers were scanned

using a CBCT system (80μm) and a μCT system (35μm). Three bone microstructural

parameters trabecular number (Tb.N), thickness (Tb.Th) and separation (Tb.Sp) were

assessed using CTAn imaging software.

Results: Intraclass correlation coefficients (ICC) showed a high intra-observer

reliability (≥ 0.996) in all parameters for both systems. The Pearson correlation

coefficients between the measurements of the two systems were for Tb.Th 0.82, for

Tb.Sp 0.94 and for Tb.N 0.85 (all P's

27

Introduction

The influence of bone quality on implant success is well acknowledged. Information of bone

quality is best gained by combining bone mineral density (BMD) and trabecular structure

assessments (Felsenberg & Boonen 2005). Trabecular bone is the source of osteoblasts and

osteoclasts which are largely responsible for the physiological changes which take place

subsequent to implant placement (Minkin & Marinho 1999).

The role of trabecular

microstructure in dental implants is significant since the implant is surrounded by trabecular

bone which directly contributes to implant stability (Fanuscu & Chang 2004; Sakka &

Coulthard 2009).

Radiographic information of the trabecular microstructure can be achieved by using high

resolution imaging modalities that approach the trabecular dimensions (50-300μm).

However, most of the utilized modalities have limitations in clinical practice. High resolution

radiographic systems’ limitations rest on excessive radiation exposure to the patient (MDCT,

HR-pQCT), complex image analysis (hr-MRI), restricted accessibility (MDCT, hr MRI) and

limited scanning sites (HR-pQCT, µCT) (Genant et al. 2000; Issever et al. 2010).

Since 2001, cone-beam CT (CBCT) has been used to provide three-dimensional images for

diagnosis and treatment planning in dentistry (Hatcher 2010). It provides comparable images

at reduced scan costs and dose. Additionally, CBCT systems are largely accessible to dental

professionals and the scan time is relatively short (Patcas et al. 2012). Numerous CBCT

studies were conducted using CBCT for dental implants. However, up to date the focus has

mainly been on bone quantity (alveolar bone width and height) measurement accuracy, bone

density, visibility of anatomical landmarks and virtual guided surgery (Quereshy et al. 2008;

Tahmaseb et al. 2011). Recently, CBCT has been suggested as a modality for analysing

trabecular microstructure at implant sites (Corpas et al. 2011; Ibrahim et al. 2012). To the best

of our knowledge, there has been no report validating the ability of this system in measuring

trabecular bone microstructural parameters. Therefore, the aim of this study was to determine

the accuracy of CBCT in trabecular bone microstructure measurement in comparison with

µCT, after first assessing the intraobserver reliability.

28

Materials and methods

Image acquisition

Twenty-four human mandibular cadavers were obtained from the Department of Functional

Anatomy. Approval was obtained from the department to use this material for research

purposes. All cadavers were scanned using a CBCT system (3D Accuitomo 170, Morita,

Japan). To obtain the highest spatial resolution possible of an isotropic 80µm, the smallest

field of view (FOV), that is, 4x4cm with the high-resolution scan mode and 360º arm

rotation, were the selected scan settings. Images were acquired at 90kv and 5.0mA. The

cadavers were then scanned using a µCT system (SkyScan 1173, Kontich, Belgium). This

system allows scanning of large size specimens (140mm in diameter, 200mm in height).

Hence the mandibles were not sectioned into smaller sizes. To reduce any possible

movements of the samples during scanning, the samples were fixed in a cylindrical shape of

Styrofoam, fit and mounted into the holder before scanning. The resultant images were again

checked for motion artifacts. The settings were set by the manufacturer operating at 130kVp,

61mA with nominal isotropic resolution of 35µm.

Figure 1: A mandible scanned using cone beam CT (CBCT) was cropped into a smaller

block in Amira software.

29

Image processing

The datasets from both systems were exported as DICOM 3 files and imported into image

analysis software (Amira v4.1, Visage Imaging Inc., Carlsbad, CA). To select the exact

region of interest (ROI) from both systems, the datasets were processed as follows. The three-

dimensional (3D) isosurfaces datasets from both µCT and CBCT were created and saved as

STL files. Subsequently, the mandibles were cropped (confined) to the edentulous posterior

region (Fig. 1). The z-axis of the CBCT images was flipped to match that of µCT because the

scan orientation differs between the two systems. The isosurfaces were used to provide

coordinates for matching the original volumes (voxel data) of both systems. The bone blocks

were then manually superimposed on each other to provide maximum alignment (Fig. 2). The

µCT was set as the reference standard while CBCT was considered as the experimental

modality. For each matching set, a smaller ROI was selected confined in the vertical and

horizontal slice directions to the cortical bone margins. For µCT ten and for CBCT five

consecutive and contiguous slices were chosen for evaluation. Since µCT isotropic voxel size

(35µm3) is approximately half of that for CBCT (80µm

3), a double amount of slices was

selected to ensure that the measurements are from the same region. The selected slices were

exported as BMP images and imported into trabecular bone analysis software CTAn (v 1.11,

SkyScan, Kontich, Belgium). Through histogram analysis, the images were thresholded and

binarized (Fig. 3). The selected CBCT and micro CT image slices were then averaged to

reduce the bias that may occurred during the manual registration procedure in Amira

software. Twenty-four ROI were carefully matched and compared prior to the measurement

process (Fig. 4). The regions disturbed by metal artifacts or cut off during the µCT scanning

process were excluded. All measurements were performed by one trained observer twice for

both systems independently with at least one week interval between the first and the second

measurement to assess the observer’s bias.

30

Figure 2: A block of cropped

CBCT image was manually

transformed, superimposed

and matched onto µCT to

select the region of interest.

(ROI).

Figure 3: Binarized images of

CBCT (a) and µCT (b).

Figure 4: Trabecular bone

microstructure parameters

were measured and

compared using CBCT (A)

and µCT (B) datasets.

31

Statistical analysis

The bone microstructural parameters that were considered for evaluations were the trabecular

number (Tb.N), thickness (Tb.Th) and separation (Tb.Sp). The intraclass correlation

coefficient (ICC) was used to determine the intraobserver reliability in measuring trabecular

microstructure using CBCT and µCT. Paired t-tests were used to assess the difference in

means between the two systems and the linear relation between both systems was assessed

using the Pearson correlation coefficient (r). Finally a Bland-Altman plot was used to assess

the accuracy of CBCT in measuring the microstructural parameters by plotting the difference

between the measurements of CBCT and µCT against the means of those measurements (Fig.

5).

Figure 5: Bland-Altman plot of three bone microstructural parameters trabecular number

(Tb.N), thickness (Tb.Th) and separation (Tb.Sp). Tb.Th (a), Tb.Sp (b) and Tb.N (c)

measurements between CBCT and µCT.

32

Results

The ICC’s revealed excellent intraobserver reliability for all parameters and both systems

separately (Table 1). Therefore the first measurement was used for the following analyses.

Paired t-tests showed significant differences between the CBCT and µCT for all parameters.

Tb.Th and Tb.Sp were higher on CBCT images than on µCT images, but Tb.N was lower by

CBCT (Table 2). The Pearson correlation coefficients for the relation between CBCT and

µCT were all significant at p< 0.001 (Table 2). Bland-Altman analysis showed the smallest

bias of measurements in Tb.N (-0.37 µm-1

) followed by Tb.Th (1.6µm) and Tb.Sp (8.8µm).

The confidence interval for the measurement differences between the two systems was

smallest for Tb.N (-0.79 to +0.06), followed by Tb.Th (-2.1 to +5.3) and Tb.Sp (-21.4 to

+43.1).

Table 1. Intraobserver reliability for CBCT and µCT using the intraclass correlation

coefficient (ICC).

N=24

ICC

CBCT µCT

Tb.Th 0.998 0.999

Tb.Sp 0.997 0.999

Tb.N 0.999 0.996

Three bone microstructural parameters: trabecular number (Tb.N), thickness (Tb.Th) and

separation (Tb.Sp).

33

Table 2: Structural parameters, paired t-test, and correlation between measurements obtained

by CBCT and µCT.

Parameters Means and standard

deviation

Paired t-test Pearson ( r2 )

CBCT µCT means sd SEM t df p

Tb.Th (µm) 0.41 + 0.15 0.28 +0.10 0.13 0.08 0.02 7.48 23 0.001 0.67*

Tb.Sp (µm) 0.85 +0.44 0.71 +0.39 0.15 0.15 0.03 4.83 23 0.001 0.89*

Tb.N (µm-1) 0.87 +0.26 1.11 +0.29 -0.24 0.16 0.03 -7.35 23 0.001 0.72*

Three bone microstructural parameters: trabecular number (Tb.N), thickness (Tb.Th) and

separation (Tb.Sp).

* Correlation is significant at the 0.001 level

Discussion

Bone quality evaluations using CBCT are usually concentrated on bone density (Corpas et al.

2011; Gonzalez-Garcia & Monje 2012; Ibrahim et al. 2012; Parsa et al. 2012). However, in

order to understand its influence on implant-bone integration, bone quality should also be

assessed from the microstructural aspect of trabecular bone (Fanuscu & Chang 2004;

Gonzalez-Garcia & Monje 2012). Apart from its role in bone healing and implant retention

(Minkin & Marinho 1999), trabecular bone microstructure also contributes to bone strength

(Felsenberg & Boonen 2005; Manske et al. 2010). Amongst the recommended

microstructural parameters to estimate bone strength are trabecular thickness (Tb.Th),

number (Tb.N), and spacing between each other (Tb.Sp). Bone density and trabecular

microstructure measurements do not always correlate with each other (Gomes de Oliveira et

al. 2012). For instance, a low-density bone may be associated with low Tb.Th (Hildebrand et

al. 1999; Ranjanomennahary et al. 2011) and Tb.N, but high Tb.Sp (Hans et al. 2011;

Ranjanomennahary et al. 2011). But, after a series of medications in osteoporotic patients, the

increased bone density did not improve its bone strength (Riggs et al. 1990). Therefore, in

selecting the best bone quality prior to dental implant treatment, the trabecular microstructure

should also be included as part of the pre-surgical bone assessment.

34

µCT is a gold standard modality for trabecular microstructural assessment. However, it can

only evaluate small bone samples and it cannot be employed to scan patients in a clinical

setting (Burghardt et al. 2011). The use of CBCT in dental implants has increased, but its

ability in analyzing the bone microstructural parameters at implant receptor site remains

unverified. At present, only few studies reported that CBCT has potentials in measuring

trabecular microstructure (Corpas et al. 2011; Liu et al. 2007; Ibrahim et al. 2013). In this

study, Tb.N, Tb.Th and Tb.Sp at posterior mandibular regions were directly assessed,

analyzed and compared between CBCT and µCT datasets using human mandibular cadavers

at implant receptor sites. A strong correlation was observed when comparing the trabecular

bone microstructure measurements with µCT. Bland-Altman plots show strong agreements

between CBCT and µCT when measuring Tb.N (-0.37µm), Tb.Th (1.6µm) and Tb.Sp

(8.8µm). Only Tb.N was measured smaller by CBCT (indicated by a negative bias value)

while Tb.Th and Tb.Sp were measured greater than µCT (indicated by positive bias values).

This discrepancy is in accordance to the voxel size of CBCT (80µm) which is twice that of

µCT (35µm). Since the voxel in CBCT is significantly larger than its counterpart in µCT, the

system can only depict significantly thick trabeculae, which in turn results in a smaller

trabecular number measurement. This is in accordance with a study on trabecular

microstructural parameters which also concluded that small trabeculae are poorly depicted by

low resolution systems when comparing MDCT (274µm) with µCT (16µm) (Issever et al.

2010) and HR-pQCT (82µm) with µCT (18µm) (Tjong et al. 2012). The latter study

described that partial volume effects (PVE) were increased and a rough estimation of Tb.Th

was observed when bigger voxel size images were utilized when measuring microstructural

parameters. However, it has to be emphasized that while spatial resolution expressed in voxel

size is a limiting factor for image quality, it’s not the only contributing factor. Contrast to

noise ratio (CNR) plays an equally important role in determining the capability of an imaging

system for depicting delicate anatomical structures. A sufficiently high CNR can possibly

compensate for a relatively low spatial resolution (Bechara et al. 2012). Therefore, thin

trabeculae can be made visible even when the trabecular thickness itself is beyond the

nominal voxel size. Image quality is also largely affected by micro-movement of the jaw. The

stationary situation of the cadavers in the present study might have thus enhanced the

visibility of the trabecular structures because the scans did not suffer from patient’s

movement.

35

One difficulty in this study was triggered during identifying the ROI. The projections in

CBCT were not completely similar to µCT because the mandibles were positioned vertically

in µCT to meet the space constraints in the scanner. Through using 3D virtual

reconstructions, CBCT and µCT were brought into maximum alignment. The measurements

could still somewhat vary due to the limitation of the non-automated registration. However,

when an automated registration is not attainable, Diederichs et al. (2008) had suggested that

both scans should be performed at a similar position to improve the accuracy of the selected

ROIs. Further, prior to microstructural assessment in CTAn software, the visualized

trabecular bone was again compared slice by slice to ascertain that the ROI was

correspondingly selected.

The size of the ROI and density of the bone strongly influence trabecular bone assessments.

A diameter of 5mm or larger bone specimen is recommended to adequately analyze bone

parameters (Vigorita 1984). In this study we used the full height and width of the bone

sections sampled at 5 and 10 consecutive and contiguous coronal slices for CBCT and µCT,

respectively (Fig. 3). Nevertheless, this careful sampling still does not guarantee that all

measurements were from anatomically identical regions. This is due to the manual nature of

aligning the two datasets and the possibility for observer error. An automated, observer-

independent 3D volumetric matching using software tool is recommended to improve the

alignment accuracy (Li et al. 2008). However, this was not possible in this study due to

technical constrains. At any rate, the measurements were repeated twice and the ICC results

show strong agreements between the first and the second measurement (Table 1).

Apart from voxel size (Tjong et al. 2012); the accuracy of structural measurement is also

influenced by the threshold selection (Parkinson et al. 2008). Bone segmentation with CBCT

remains difficult as image quality is affected by artifacts specific to the scanning technology

(Parsa et al. 2013; Schulze et al. 2011). CBCT has a lower signal to noise ratio than µCT, a

larger amount of scatter radiation, reduced contrast and is largely susceptible to beam

hardening and edge aliasing artifacts (Schulze et al. 2011). All these combined factors

introduce errors in the grey level values in CBCT which results in underestimation of thin

trabecular bone. Therefore, it was recommended to use denser bone specimens to overcome

this problem (Wirth et al. 2012). In this study all 24 mandibles were used regardless of their

density. Owing to its extremely high resolution, µCT was able to image bone specimens even

for poor bone density blocks. However, the same could not be stated for CBCT since the

36

partial volume effect is accentuated for sparse trabecular patterns. Therefore, trabecular

spacing in particular was overestimated since many small trabeculae were thresholded out

when binarizing the images. This could possibly explain the outliers depicted in the Bland-

Altman plots (Fig. 5). Only small discrepancies were observed (Tb.N

37

References:

1. Bechara, B., McMahan, C.A., Moore, W.S., Noujeim, M., Geha, H. & Teixeira, F.B.

(2012) Contrast- to-noise ratio difference in small field of view cone beam computed

tomography machines. Journal of Oral Science 54: 227–232.

2. Burghardt, A.J., Link, T.M. & Majumdar, S. (2011) High-resolution computed

tomography for clinical imaging of bone microarchitecture. Clinical Orthopaedics

and Related Research 469: 2179–2193.

3. Corpas, L.S., Jacobs, R., Quirynen, M., Huang, Y., Naert, I. & Duyck, J. (2011) Peri-

implant bone tissue assessment by comparing the outcome of intra-oral radiograph

and cone beam computed tomography analyses to the histological standard. Clinical

Oral Implants Research 22: 492-499.

4. Diederichs, G., Link, T., Marie, K., Huber, M., Rogalla, P., Burghardt, A., Majumdar,

S. & Issever, A. (2008) Feasibility of measuring trabecular bone structure of the

proximal femur using 64-slice multidetector computed tomography in a clinical

setting. Calcified Tissue International 83: 332–341.

5. Fanuscu, M.I. & Chang, T.L. (2004) Three-dimensional morphometric analysis of

human cadaver bone: microstructural data from maxilla and mandible. Clinical Oral

Implants Research 15: 213-218.

6. Felsenberg, D. & Boonen, S. (2005) The bone quality framework: determinants of

bone strength and their interrelationships, and implications for osteoporosis

management. Clinical Therapeutics 27: 1–11.

7. Genant, H.K., Gordon, C., Jiang, Y., Link, T.M., Hans, D., Majumdar, S. & Lang,

T.F. (2000) Advanced imaging of the macrostructure and microstructure of bone.

Hormone Research 54 (suppl 1): 24-30.

8. Gomes de Oliveira, R.C., Leles, C.R., Lindh, C. & Ribeiro-Rotta, R.F. (2012) Bone

tissue microarchitectural characteristics at dental implant sites. Part 1: identification

of clinical-related parameters. Clinical Oral Implants Research 23: 981–986.

9. Gonzalez-Garcia, R. & Monje, F. (2012) The reliability of cone-beam computed

tomography to assess bone density at dental implant recipient sites: a

histomorphometric analysis by micro-CT. Clinical Oral Implants Research doi:

10.1111/j.1600-0501.2011.02390.x.

10. Hans, D., Barthe, N., Boutroy, S., Pothuaud, L., Winzenrieth, R. & Krieg, M.A.

(2011) Correlations between trabecular bone score, measured using anteroposterior

38

dual-energy X-ray absorptiometry acquisition, and 3-dimensional parameters of bone

microarchitecture: an experimental study on human cadaver vertebrae. Journal of

Clinical Densitometry 14: 302–312.

11. Hatcher, D.C. (2010) Operational principles for cone-beam computed tomography.

Journal of American Dental Association 141(suppl 3): 3–6.

12. Hildebrand, T., Laib, A., Müller, R., Dequeker, J. & Ruegsegger, P. (1999) Direct

three-dimensional morphometric analysis of human cancellous bone: microstructural

data from spine, femur, iliac crest, and calcaneus. Journal of Bone Mineral Research

14: 1167–1174.

13. Ibrahim, N., Parsa, A., Hassan, B., van der Stelt, P. & Wismeijer, D. (2012)

Diagnostic imaging of trabecular bone microstructure for oral implants: a literature

review. Dentomaxillofacial Radiology 42:20120075.

14. Issever, A.S., Link, T.M., Kentenich, M., Rogalla, P., Burghardt, A.J., Kazakia, G.J.,

Majumdar, S. & Diederichs, G. (2010) Assessment of trabecular bone structure using

MDCT: comparison of 64- and 320-slice CT using HR-pQCT as the reference

standard. European Radiology 20: 458-468.

15. Li, G., Xie, H., Ning, H., Citrin, D., Capala, J., Maass-Moreno, R., Guion, P., Arora,

B., Coleman, C., Camphausen, K. & Miller, R. (2008) Accuracy of 3D volumetric

image registration based on CT, MR and PET/CT phantom experiments. Journal of

Applied Clinical Medical Physics 9: 17-36.

16. Liu, S.M., Zhang, Z.Y., Li, J.P., Liu, D.G. & Ma, X.C. (2007) A study of trabecular

bone structure in the mandibular condyle of healthy young people by cone beam

computed tomography. Zhonghua Kou Qiang Yi Xue Za Zhi 42: 357-360.

17. Manoglas, S.C. & Jilka, R.L. (1995) Bone marrow, cytokines, and bone modelling.

Emerging insights into the pathophysiology of osteoporosis. New England Journal of

Medicine 332: 305-311.

18. Manske, S.L., Macdonald, H.M., Nishiyama, K.K., Boyd, S.K. & McKay, H.A.

(2010) Clinical tools to evaluate bone strength. Clinical Reviews in Bone and Mineral

Metabolism 8: 122–134.

19. Minkin, C. & Marinho, V.C. (1999) Role of the osteoclast at the bone-implant

interface. Advances in Dental Research 13: 49–56.

20. Parkinson, I.H., Badiei, A. & Fazzalari, N.L. (2008) Variation in segmentation of

bone from micro-CT imaging: implications for quantitative morphometric analysis.

Australasian Physical & Engineering Sciences in Medicine 31: 160-164.

39

21. Parsa, A., Ibrahim, N., Hassan, B., Motroni, A., van der Stelt, P. & Wismeijer, D.

(2013) Influence of cone beam CT scanning parameters on grey value measurements

at implant site. Dentomaxillofacial Radiology 42:79884780.

22. Patcas, R., Markic, G., Müller, L., Ullrich, O., Peltomäki, T., Kellenberger, C.J &

Karlo C.A. (2012) Accuracy of linear intraoral measurements using cone beam CT

and multidetector CT: A tale of two CTs. Dentomaxillofacial Radiology 41: 637–644.

23. Quereshy, F.A., Savell, T.A. & Palomo, J.M. (2008) Applications of cone beam

computed tomography in the practice of oral and maxillofacial surgery. Journal of

Oral Maxillofacial Surgery 66: 791-796.

24. Ranjanomennahary, P., Ghalila, S.S., Malouche, D., Marchadier, A., Rachidi, M.,

Benhamou, C. & Chappard, C. (2011) Comparison of radiograph-based texture

analysis and bone mineral density with three-dimensional microarchitecture of

trabecular bone. Medical Physics 38: 420–428.

25. Riggs, B.L., Hodgson, S.F., O’Fallon, W.M., Chao, E.Y., Wahner, H.W., Muhs, J.M.,

Cedel, S.L. & Melton, J.M. (1990) Effect of fluoride treatment on the fracture rate in

postmenopausal women with osteoporosis. New England Journal of Medicine 332:

802–809.

26. Sakka, S. & Coulthard, P. (2009) Bone quality: a reality for the process of

osseointegration. Implant Dentistry 18: 480-485.

27. Schulze, R., Heil, U., Gross, D., Bruellmann, D.D., Dranischnikow, E., Schwanecke,

U. & Schoemer, E. (2011) Artefacts in CBCT: a review. Dentomaxillofacial

Radiology 40: 265-273.

28. Tahmaseb, A., De Clerck, R., Eckert, S. & Wismeijer, D. (2011) Reference-based

digital concept to restore partially edentulous patients following an immediate loading

protocol: a pilot study. International Journal of Oral Maxillofacial Implants 26: 707-

717.

29. Tjong, W., Kazakia, G.J., Burghardt, A.J. & Majumdar, S. (2012) The effect of voxel

size on high-resolution peripheral computed tomography measurements of trabecular

and cortical bone microstructure. Medical Physics 39: 1893-1903.

30. Vigorita, V.J. (1984) The bone biopsy protocol for evaluating osteoporosis and

osteomalacia. The American Journal of Surgical Pathology 8: 925-930.

31. Wirth, A.J., Müller, R., van Lenthe, G.H. (2012) The discrete nature of trabecular

bone microarchitecture affects implant stability. Journal of Biomechanics 45: 1060-

1067.

40

41

Chapter 3

Bone quality evaluation at dental

implant sites using Multi-slice CT,

Micro-CT and CBCT

This chapter has been published as:

Parsa A, Ibrahim N, Hassan B, van der Stelt P, Wismeijer D. Bone quality evaluation

at dental implant site using Multi-slice CT, Micro-CT and Cone Beam CT. Clinical

Oral Implants Research. doi: 10.1111/clr.12315.

42

Bone quality evaluation at dental implant site using

Multi-slice CT, Micro-CT and Cone Beam CT

Azin Parsa, Norliza Ibrahim, Bassam Hassan, Paul van der Stelt,

Daniel Wismeijer.

Clinical Oral Implants Research. doi: 10.1111/clr.12315.

Summary

Objectives: The first purpose of this study was to analyze the correlation between

bone volume fraction (BV/TV) and calibrated radiographic bone density (HU) in

human jaws, derived from micro-CT and MSCT respectively. The second aim was to

assess the accuracy of CBCT in evaluating trabecular bone density and microstructure

using MSCT and micro-CT, respectively, as reference gold standards.

Material and methods: Twenty partially edentulous human mandibular cadavers

were scanned by three types of CT modalities: MSCT (Philips, Best, the Netherlands),

CBCT (3D Accuitomo 170, J.Morita, Kyoto, Japan), and micro-CT (SkyScan 1173,

Kontich, Belgium). Image analysis was performed using Amira (v4.1, Visage Imaging

Inc., Carlsbad, CA), 3Diagnosis (v5.3.1, 3diemme, Italy), Geomagic (studio® 2012,

Morrisville, NC), and CTAn (v1.11, SkyScan, Kontich, Belgium). MSCT, CBCT, and

micro-CT scans of each mandible were matched to select the exact region of interest

(ROI). MSCT HU, micro-CT BV/TV, and CBCT grey value and bone volume

fraction of each ROI were derived. Statistical analysis was performed to assess the

correlations between corresponding measurement parameters.

Results: Strong correlations were observed between CBCT & MSCT density (r=0.89)

and between CBCT and micro-CT BV/TV measurements (r=0.82). Excellent

correlation was observed between MSCT HU and micro-CT BV/TV (r=0.91).

However significant differences were found between all comparisons pairs (p

43

Conclusions: An excellent correlation exists between bone volume fraction and bone

density as assessed on micro-CT and MSCT, respectively. This suggests that bone

density measurements could be used to estimate bone microstructural parameters. A

strong correlation was also found between CBCT grey values and BV/TV and their

gold standards, suggesting the potential of this modality in bone quality assessment at

implant site.

44

Introduction

Primary implant stability is the key factor for the long term success of an implant treatment

by improving osseointegration (Fuh et al. 2010). Primary instability of an implant induces

movements during healing. This micro-motion leads to fibroplasia as a biological response at

bone tissue surrounding the implant. The replacement of bone by fibrous tissue and loss of

osseointegration cause implant failure (Lioubavina-Hack et al 2006). Bone quality, which

refers to the combination of all bone characteristics that influence bone resistance to fracture

(Fyhrie 2005), is one of the most important factors influencing primary implant stability

(Ozan et al. 2007; Tolstunov 2007). Among the bone characteristics, bone mineral density

(BMD) and trabecular microstructure are the strongest predictors for bone strength (Muller

2003). However, these two parameters need to be simultaneously assessed to provide better

estimation of bone strength (Teo et al. 2007; Diederichs et al. 2009).

Several radiographic modalities have been used for bone quality assessment. For bone

microstructure, micro-computed tomography (micro-CT) was recommended as gold standard

for assessing bone morphology and micro architecture (Burghardt et al. 2011; Ibrahim et al.

2013a). However, it is limited to ex-vivo small bone samples and cannot be employed for

patients. Multiple X-ray projections with different angles in micro-CT allow a precise three-

dimensional (3D) reconstruction of the bone samples and assessment of bone trabeculae

(Martin-Badosa et al. 2003). Micro-CT is used to measure several histomorphometric

variables including bone volume (BV), total volume (TV), bone volume fraction (BV/TV),

trabecular thickness (Tb.Th), trabecular number (Tb.N) and trabecular separation (Tb.Sp)

(Odgaard 1997).

For bone density, multislice computed tomography (MSCT) is an established clinical

modality in which calibrated Hounsfield units (HU) can accurately be converted to BMD

measurements (Shahlaie et al. 2003; Shapurian et al. 2006). However, higher radiation

exposure risk to patients in comparison with other modalities remains a main concern for

applying MSCT for assessing bone quality (Dula et al. 1996; Ekestubbe et al. 1992;

Ekestubbe et al. 1993; Frederiksen et al. 1995). Cone Beam Computed Tomography (CBCT),

due to increased accessibility to dental practitioners, more compact equipment and reduced

cost and radiation dose, has widely replaced medical CT for oral and maxillofacial imaging.

45

Several studies reported high geometric accuracy of CBCT for linear measurement

(Lagravère et al. 2008; Lou et al. 2007; Naitoh et al. 2004), while its reliability in bone

quality evaluation remains controversial. Only few studies suggested that CBCT could be

applied to assess trabecular bone microstructure (Corpas Ldos et al. 2011; Liu et al. 2007).

Additionally, CBCT does not represent calibrated voxel grey values expressed in HU (Hua et

al. 2009).

Yet, many attempts have been conducted to assess the feasibility of converting

CBCT grey values to actual density measurements. High correlation between HU derived

from MSCT and CBCT voxel grey values has been demonstrated , hinting at the potential of

CBCT in bone density assessment (Aranyarachkul et al. 2005; Cassetta et al. 2013; Lagravère

et al. 2006; Naitoh et al. 2009; Naitoh et al. 2010b; Nomura et al. 2010; Parsa et al. 2012;

Reeves et al. 2012). However, the excessive scattering and technology-specific artifacts

produced in CBCT have been denoted as the perpetrator for the unreliable BMD

measurements (Araki et al. 2011; Hua et al. 2009; Nackaerts et al. 2011; Schulze et al. 2011;

Yoo & Yin 2006).

High correlation between bone volume fraction (BV/TV) provided by micro-CT and voxel

grey value from CBCT, and also between bone volume fraction derived from CBCT and CT

numbers from MSCT has been reported (González-García & Monje 2012; Naitoh et al.

2010a). However the relation between bone volume fraction and radiographic bone density in

human jaws remains controversial (Aksoy et al. 2009; Stoppie et al. 2006). Therefore, the

first purpose of this study was to analyze the correlation between bone volume fraction

(BV/TV) and calibrated radiographic bone density (HU) in human jaws, derived from micro-

CT and MSCT respectively. The second aim was to assess the accuracy of CBCT in

evaluating trabecular bone density and microstructure using MSCT and micro-CT,

respectively, as reference gold standards.

Material and methods

Sample preparation and radiographic evaluation

Twenty partially edentulous human mandibular cadavers not identified by age, sex or ethnic

group were obtained from the functional anatomy department. The cadavers were sectioned

at the mid-ramus level and fixed in formaldehyde (formaldehyde 74.79%, Glycerol 16.7%,

Alcohol8.3 %, and Phenol 0.21%) and stored. A declaration was obtained from the

Functional Anatomy department to use this human remains material for research purposes.

46

The restorative materials which can produce artifact such as amalgam filling and metal

crowns were removed from dentitions. The mandibles were scanned by three types of CT

modalities: MSCT (Philips, 120 kVp, 222 mA, 1.128 S, 0.67mm isotropic voxel size, Best,

the Netherlands), CBCT (3D Accuitomo 170, 90 kVp, 5 mA, 30.8 S, 4x4 cm FOV, 0.08 mm

isotropic voxel size, J.Morita, Kyoto, Japan), and micro-CT (SkyScan 1173,130 kVp, 61 mA,

35 min, 35μm isotropic voxel size, Kontich, Belgium). In MSCT scans, the occlusal plane of

each mandible was set perpendicular to the floor with zero gantry tilt, whereas in CBCT

scans it was set parallel to the floor according to manufacturer’s recommended protocol. The

edentulous region of each mandible was located at the center of FOV in CBCT scans. Owing

to the large gantry of applied micro-CT (140mm in diameter, 200mm in height), mandibles

were not sectioned to smaller samples. To prevent the possible micro movements during the

scanning due to the large size of the samples, a cylindrical shape Styrofoam was used to fix

and mount the sample into the holder.

Image processing

All CT data sets were converted to Digital Imaging and Communication in Medicine

(DICOM3) format. As the scan orientation differs between micro-CT and the other two

systems, the z-axis of CBCT and MSCT images were flipped to match that of micro-CT for

further procedures. Micro-CT data sets were large in size, therefore was not possible to be

flipped by our workstation. Image analysis was performed using Amira (v4.1, Visage

Imaging Inc., Carlsbad, CA), 3Diagnosis (v5.3.1, 3diemme, Italy), Geomagic (studio® 2012,

Morrisville, NC), and CTAn (v1.11, SkyScan, Kontich, Belgium). MSCT images were

imported to 3Diagnosis software. Two cylindrical shape virtual probes (with diameter and

height of 0.7 and 8 mm, respectively) were inserted at the edentulous region within the

cancellous bone, with 3 mm bucco-lingual distance between them (Figure1a, b). These

probes were used as indicators to facilitate the selection of exact region of interest (ROI)

from MSCT, CBCT and micro-CT. For MSCT, the probes were visible in a single cross-

sectional slice since the voxel size of MSCT scans was 0.67 mm, which is thick enough to

allow the probes to be visible in one slice. Subsequently, a rectangular area was drawn

between the two probes from the slice of interest to define the ROI for density measurements.

47

Figure 1. (a) Three-dimensional reconstruction of a mandible MSCT scan with inserted

probes. (b) close-up view of probes (C) three-dimensional reconstruction of a mandible

CBCT scan with transferred probes.

The mean HU value from each ROI was calculated. All ROIs were totally within the

cancellous bone, excluding cortical bone, inferior dental canal and any large bone defect.

For CBCT, a volume-based 3D registration algorithm using Geomagic software was applied

to transform the inserted probes from the MSCT datasets to the CBCT scans. A standard

triangulation language (STL) surface file of the MSCT and CBCT scans were matched and

the probes were transferred from MSCT scans to the exact region on CBCT’s (Figure1c). As

a result, new CBCT datasets which include the probes were obtained. Using 3Diagnosis eight

consecutive slices passing through the probes were selected from each CBCT dataset to

calculate the mean grey values (radiological density). This is because slice thickness in

CBCT is 0.08mm which approximately amounts to 8 times thinner than the equivalent slice

thickness in MSCT. A rectangular region was also drawn between the two probes similar to

MSCT and grey values from corresponding anatomical locations were derived.

CBCT radiological density of each mandible’s ROI was considered as the mean of eight

calculated grey values. Subsequently, the selected ROIs were saved as a bitmap (BMP) image

files to allow the trabecular micro-structure evaluation.

Using Amira, each micro-CT scan was cropped to have a smaller sample including the ROI.

Due to large micro-CT data sets, the superimposition of CBCT and micro-CT scans was done

as follows: maximum alignment of both datasets was obtained by manually matching and

superimposition of isosurfaces generated in Amira software. Subsequently, sixteen micro-CT

slices (correspondence to the eight CBCT slices) were selected and saved as a 16-bitmap

(BMP) image file (65536 gray values). Then, these bmp files were exported to CTAn

software for trabecular microstructure evaluations (Figure 2a). A rectangular ROI for

48

trabecular was selected on each dataset slice by slice (Figure 2b). All images were

thresholded using an automated histogram analysis and binarized (Figure 2c) to allow the

measurement process. On micro-CT datasets, the ROI was again verified by carefully

comparing slices with CBCT’s (as reference). This was performed to reduce bias which may

have been introduced during the manual superimposition of the two datasets. All

measurements were performed twice with one month interval by a trained maxillofacial

radiologist.

Figure 2. (a) Images of micro-CT and CBCT were compared slice by slice from the same

anatomical region. (b) A rectangular region of interest (ROI) was selected for each dataset.

(c) Images were binarized and (d) processed to allow the microstructural measurements.

49

Data analysis

Statistical analysis was performed using SPSS (v17.0, SPSS Inc., Chicago, IL). To determine

the intraobserver reliability of the radiological and microstructural density measurement,

intraclass correlation coefficient (ICC) was used. The Shapiro-Wilk test was used to verify

the normality of the data. Paired t-test was used to assess the mean difference between MSCT

and CBCT density measurements and between CBCT BV/TV and micro-CT BV/TV, while

Pearson correlation coefficient was used to assess the linear relation between corresponding

measurement parameters. Finally a Bland-Altman plot was used to assess the accuracy of

CBCT in measuring trabecular BMD and bone microstructural density by plotting the

difference between the measurements of CBCT against MSCT density and microCT BV/TV

against the means of the compared measurements.

Results

Excellent intraobserver reliability (ICC ≥ 0.97) was revealed for repeated measurements in

the three systems. Therefore, the mean of two measurements was calculated for further

analysis. The mean HU of the selected ROI ranged from -60 to 507.6 (mean 222.85 &

standard deviation [SD] 140.5) while CBCT grey values ranged from161.6 to 665.6 (mean

377.49 & SD 127.4). The negative HU derived from MSCT for case 4, 16 and 20 (Table 1)

may indicate fat in trabecular spaces (Parsa et al. 2012). Calculated BV/TV of the same ROI

ranged from 2.24 to 75.83 (mean 32.35 & SD 18.81) for micro-CT and from 3.73 to 68.72

(mean 36.79 & SD 23.17) for CBCT (Table1). Paired t-test showed significant differences (p

< 0.001) between all comparison pairs except for mean measurement between CBCT BV/TV

and micro CT BV/TV (p=0.147) . In all selected ROIs, CBCT showed a higher density than

MSCT HU and a higher BV/TV than that of micro-CT. The normal distribution of

measurements was confirmed by visually inspecting the histogram and the result of the

Shapiro-Wilk test (p > 0.05). Therefore, the use of the t-test and Bland-Altman test is

justified. Strong correlations were observed between CBCT and MSCT density

measurements (r=0.89) and between CBCT and micro-CT BV/TV measurements (r=0.82).

Excellent correlation was observed between MSCT HU and micro-CT BV/TV (r=0.91).

Bland-Altman analysis showed the bias in measuring BV/TV between CBCT and micro-CT

is smaller (4.44µm-1

) than measuring the density between CBCT and MSCT (154.65HU)

(Figure 3a, b). The 95% measurement errors are between -21.31 to 30.19 for BV/TV and

50

29.74 to 279.56 for density measurement. The differences of CBCT and micro-CT BV/TV

measurements were minimal (4.44µm-1

), suggesting strong agreement.

Table1. Mean results of MSCT, Micro-CT and CBCT density (grey value) and bone volume

fraction (BV/TV) measurements.

Mandible

No.

MSCT HU CBCT grey value Micro-CT

BV/TV (%)

CBCT BV/TV

(%)

1 390,9 513,3 37,11 44,43

2 205,1 444,6 41,40 46,17

3 507,6 665,6 75,83 63,58

4 -16,6 204,7 3,91 3,73

5 136 324,4 17,44 9,98

6 202,4 270,8 23,99 24,87

7 245,2 368,5 31,68 24,68

8 327,1 417,5 47,62 50,41

9 341 442,9 49,34 37,94

10 316 474,3 48,68 62,94

11 270 379,3 50,86 48,58

12 210,2 278 44,64 68,72

13 320 559,1 44,12 63,28

14 204,4 344,9 22,65 26,31

15 285,7 357 34,57 66,29

16 -27,6 161,6 2,24 4,33

17 220 483,7 24,87 55,30

18 240,4 376,7 26,97 23,21

19 139,2 285,3 15,97 6,68

20 -60 197,7 3,16 4,45

51

Figure 3. Bland-Altman plots of (a) BV/TV measurements between CBCT and µCT, and (b)

density measurements between CBCT and MSCT.

Discussion

It has been proven that the success of an inserted implant strongly depends on the quality,

beside the quantity, of the surrounded bone (Jaffin & Berman 1991; Jemt et al. 1992). In

jawbones, density measurements derived from MSCT HU are highly reliable (Schwarz et al.

1987; Shapurian et al. 2006). However, bone density alone does not fully represent bone

quality, and should be considered together with bone microarchitecture to estimate bone

strength and fracture resistance (Diederichs et al. 2009). Histomorphometrically, bone

volume fraction, which is the trabecular bone volume (BV) per tissue volume (TV) expressed

in %, is the most important parameter (Parfitt et al. 1987). Micro-CT is accepted as a gold

standard modality for trabecular microstructure assessment, but it cannot be employed in the

clinic (Burghardt et al. 2011). In this study our aim was to investigate the possible correlation

between bone quality measurements of clinically applicable scanners in comparison with

micro-CT.

A study on porcine vertebral cancellous bone revealed a high correlation between HU derived

from CT images and BV/TV from micro-CT and suggested the use of HU from medical CT

for the prediction of microarchitecture (Teo et al. 2006). Our results support these findings

that correlation between MSCT HU and Micro-CT BV/TV is high (r=0.91). However, the

mean of calculated BV/TV in mentioned study deviated from our findings in human

52

mandibles. This could be due to different samples, ROI selections and different scanner

systems. In similar studies using human zygomatic and jawbones, a high correlation was

found only in female subjects (Aksoy et al. 2009; Nkenke et al. 2003). Thus, they suggested

that only female trabecular BV/TV can be predicted from bone mineral density. In contrast,

another study found a high correlation between BV/TV and HU in trabecular bone

surrounded by a thin layer of cortical bone regardless to gender (Stoppie et al. 2006). This

study suggested that with the development of MSCT scanners and imaging software, more

precise HU measurement would be achievable (Stoppie et al. 2006). Our results showed a

strong correlation between BV/TV and HU in human mandibular trabecular bone, regardless

to gender and thickness of surrounding cortical bone (r=0.91). This confirms the possibility

of prediction of bone volume fraction from MSCT bone density measurement. The usefulness

of this prediction can be emphasized by the limitation of micro-CT in clinical settings.

CBCT has several advantages over MSCT in terms of more compact equipment, small

footprint for the clinic, and relatively reduced scan costs. Additionally, lower radiation dose

levels to the main organs of the head and neck region have been cited as one of the most

important advantages of CBCT over MSCT (Carrafiello et al. 2010; Kau et al. 2005; White

2008). Due to these advantages, the use of this modality in dental implant planning is

growing so fast and it is more accessible to the dental practitioners than before. Therefore, the

validity of CBCT in bone quality assessment has been studied broadly. The majority of these

studies have focused on the bone density measurement and found CBCT a reliable modality

for bone density measurement (Aranyarachkul et al. 2005; Cassetta et al. 2013; Lagravère et

al. 2006; Naitoh et al. 2009; Naitoh et al. 2010b; Nomura et al. 2010; Parsa et al. 2012;

Reeves et al. 2012). The high correlation between measured CBCT grey values and CT

numbers in our study (r=0.89) may confirms the possible potential of CBCT in radiographic

density measurement. However, the limit of agreement in Bland and Altman plot (Figure 3b)

is huge (29.74 to 279.56) with a high bias value (mean = 154.64). This indicates

an unfavorable strength of agreement. Thus, although the measurements is reliable (ICC

>0.97) and validated between two compared systems (r = 0.82), the density measurement

using CBCT is less accurate when compared to its gold standard system (MSCT). It should

be considered that CBCT density measurement can be effected by scanning parameters and

the location of the ROI within the scanner (Nackaerts et al. 2011; Parsa et al. 2013).

53

Using micro-CT as gold standard, the reliability of CBCT in trabecular microstructure

assessment has been validated in human mandibles, but BV/TV was not among the assessed

microstructural parameters (Ibrahim et al. 2013b). Our results also confirm the reliability of

CBCT in trabecular microstructure assessment, based on a high correlation between BV/TV

measured by CBCT and micro-CT (r=0.82). The positive bias value (4.44µm-1

) in the Bland

and Altman plot (Figure 3a) indicates that BV/TV was measured higher by CBCT. The small

range between the confidence interval for the measurement differences between the two

systems was small (-21.31 to 30.19) indicates a strong agreement between CBCT and micro-

CT in measuring BV/TV. In present study, smallest available FOV (40x40 mm) and high

resolution scan mode were applied in CBCT scans in order to achieve the highest possible

spatial resolution (0.08 mm isotropic voxel size). Therefore using different CBCT scanning

parameters the results may differ.

It should be emphasized that the CBCT bone quality measurements in our study deviated

from those of gold standards. This deviation arises from increased scattering, noise level and

artefacts specific to the scanner technology which operates at lower peak kilovoltage and tube

loading setting than MSCT and micro-CT, resulting in a reduced signal-to-noise ratio

(Schulze et al. 2011). A higher noise level in comparison to MSCT can cause more

inconsistencies in voxel grey values (Araki & Okano 2011; Aranyarachkul et al. 2005).

Additionally, as the acquired volume in CBCT is larger than collimated fan beam in MSCT,

the influence of these artifacts is excessively exacerbated (Nackaerts et al. 2011; Schulze et

al. 2011).

Unlike the majority of other studies on bone volume fraction, our bone samples were not

harvested for micro-CT scans. As such, in our sample the possible deviation between the

planned and excised ROI, which might arise during the trepanation procedure, was eliminated

(Stoppie et al. 2006). Additionally, in the present study, a fully automated and observer

independent 3D matching algorithm was employed for MSCT and CBCT scans registration

to ensure that all measurements are exactly from the same site up to voxel accuracy.

However, due to the manual alignment of CBCT and micro-CT datasets, there is a possibility

for observer error and selection of not identical regions. Since micro-CT datasets are large

and therefore computationally expensive, technical limitations prohibited applying the 3D

registration algorithm for automated alignment. Technical advancements in the future might

resolve this issue. Finally, the difference in voxel size of CBCT (0.080 mm), micro-CT

54

(0.035mm) and MSCT (0.67 mm) can also contribute to the observed discrepancy in

calculating BV/TV and bone density.

Voxel size in CBCT influences image quality among other factors including the unit itself,

tube voltage and FOV selection (Kamburoglu et al. 2011). Generally, the smaller the voxels

the higher the spatial resolution and therefore the sharper the images appear to be. However,

small voxels result in decreased contrast to noise ratio levels and they require higher exposure

dose to the patient (Davies et al. 2012). The higher the spatial resolution the more technical

demands are imposed on the imaging system as a whole and on the imaging detector in

specific to attempt to suppress noise and increase signal levels. CBCT suffers from increased

noise levels especially at smaller voxel sizes due to low tube voltage, cone beam divergence

phenomena and inferior detector efficiency when compared to MSCT and micro-CT (Hassan

et al. 2010). However, the potential influence of varying voxel size on visibility of hard tissue

structures such as bone remains largely unknown. A recent systematic review of the literature

concluded that there is a systematic lack of evidence regarding the impact of varying voxel

size in CBCT on diagnostic performance and that possibly different voxel sizes might lead to

comparable diagnostic outcomes (Spin-Neto et al. 2013). Only one study could be identified

which demonstrated a possible effect of varying voxel size on cancellous bone measurements

in micro-CT (Yeni et al. 2005). However, it remains unknown whether the same applies to

CBCT.

In this study, a conscious effort was made to optimize image quality through selecting the

scan protocols and voxels sizes as recommended by the manufacturer for the chosen FOV’s.

Our results are limited to one CBCT system (Accuitomo 170) and results may vary on other

systems. The design specifications of different systems still vary (De Vos et al. 2009). The

lack of a technical standard for the development of CBCT systems has led to a wide disparity

in the physical parameters of each model. Developing such a standard for manufacturing

CBCT systems may help in generalizing research findings in the future. The study was also

limited as surrounding anatomical structures including the tongue and vertebra were absent.

As a result, in CBCT scans partial object artefacts resulting from structures placed outside the

scan field were not simulated. It has been previously noted that artefacts resulting from

partial sampling of objects outside the scan field could result in